Many biological processes are intrinsically dynamic, incurring profound changes at both

Many biological processes are intrinsically dynamic, incurring profound changes at both molecular and physiological levels. to other bacteria [8]. This includes numerous regulators like sigma factors which play key functions in orchestrating global gene expression pattern shifts through transcriptional regulation. Although transcriptional control remains as one of the primary means of gene expression regulation in prokaryotes, spp. are known to employ some post-transcriptional regulatory mechanisms. The best known example thus far in is usually, perhaps, the probable translational control of over 140 genes made up of a rare leucine TTA codon (including antibiotic and developmental regulators) by growth dependent expression of the sole tRNA (M145. Taking advantage of the multiplexing capability of the iTRAQ? system, we constructed time-series profiles representing protein dynamics through different growth stages in liquid culture and compared the results with microarray-derived transcriptome data. We then simplified the data using principal component analysis to evaluate the overall degree of concordance between mRNA and protein levels and to identify individual instances of significant discordant behavior. Finally, this data was mapped onto a metabolic reaction network to evaluate correlations amongst functionally related genes and interpret the biological significance of such dynamics. Results Growth kinetics and experimental setup To examine the changes in proteome profiles associated with growth and adaptation in M145 cells, we isolated total cell proteins from eight temporally spaced samples (7 h, 11 h, 14 h, 16 h, 22 h, 26 A-419259 supplier h, 34 h and 38 h) as shown in Physique 1. The samples chosen reflect changes in cellular physiology associated with growth and Eptifibatide Acetate transition to stationary phase as well as the conspicuous onsets of two prominent antibiotics, undecylprodigiosin and actinorhodin. Since the iTRAQ? system used in this study can analyze only four unique samples in A-419259 supplier a single experiment, we chose to distribute the eight protein samples to three runs of mass spectrometric analysis (Physique 1). The experiments were also designed so as to enable validation of the methodology by comparison of two protein ratios (16 h/11 h and 38 h/11 h) estimated from impartial replicate runs. Physique 1 Growth-time curve of in R5? complex media. Assessment of protein identification accuracy and quantification reproducibility Protein identifications and quantifications were carried out by searching the natural spectral data (*.wiff files) against a theoretical proteome of using ProteinPilot? software and inbuilt Paragon? search engine [13]. Decisions concerning the inclusion of single peptide (particularly single spectral evidence) hits and peptide confidence score cutoffs will greatly influence the final number of proteins one can statement. A heuristic means to arrive at these decisions is usually by estimating the false positive identification rates by performing a search against a randomized decoy database. Table 1 summarizes the results of such searches at numerous confidence levels using data from all three experimental runs. At 99% confidence level, single peptide hits incur only 3.9% false identification rate (i.e. the fraction of all single peptide hits (?=? 1100-680) that could be false based on decoy database search (?=? 18-2)). For single spectral evidence hits, a similar calculation prospects to only 4.9% false identification rate. On the other hand, the 81 (?=? 1181-1100) additional proteins identified by calming the confidence cutoff from 99% to 95% will likely include 21 (?=? 39-18) false hits giving rise to 26% false identification rate. Consequently, only the 1100 proteins recognized with 99% confidence were considered for further analysis. Biological interpretations from single peptide hits were, however, made only when additional evidences such as similar dynamic profiles from functionally related genes were available. This set of 1100 proteins corresponds to approximately 14% of the theoretical predicted proteome of of 0.081 while the 38 h to 11 h ratios estimated for 382 proteins identified in both runs 2 and 3 gave a median of 0.138. These values are A-419259 supplier comparable with those previously reported in literature for iTRAQ? experiments [14]. A small portion (3.1% and 6.8% respectively in the two comparisons) yielded a relatively high (>0.5) and as such, interpretation of protein profiles in these cases will require considerable circumspection. Nevertheless, regularity across technical replicates in these isobaric tagging LC-MS/MS experiments were at least comparable to, if not better than, those of 2D gel electrophoresis methods (median based on log imply ratios for this comparison was found to be 0.15 and a scatterplot showing this comparison is shown in supplemental data (Determine S1). A synopsis of proteins recognized and their large quantity.